Self-reported patient data has been shown to be a valuable knowledge source for post-market pharmacovigilance. In this paper we propose using the popular micro-blogging service Twitter to gather evidence about adverse drug reactions (ADRs) after firstly having identified micro-blog messages (also know as "tweets") that report first-hand experience. In order to achieve this goal we explore machine learning with data crowdsourced from laymen annotators. With the help of lay annotators recruited from CrowdFlower we manually annotated 1548 tweets containing keywords related to two kinds of drugs: SSRIs (eg. Paroxetine), and cognitive enhancers (eg. Ritalin). Our results show that inter-annotator agreement (Fleiss' kappa) for crowdsourcing ranks in moderate agreement with a pair of experienced annotators (Spearman's Rho=0.471). We utilized the gold standard annotations from CrowdFlower for automatically training a range of supervised machine learning models to recognize first-hand experience. F-Score values are reported for 6 of these techniques with the Bayesian Generalized Linear Model being the best (F-Score=0.64 and Informedness=0.43) when combined with a selected set of features obtained by using information gain criteria.

Skyline queries are a well-established technique for database query personalization and are widely acclaimed for their intuitive query formulation mechanisms. However, when operating on incomplete datasets, skylines queries are severely hampered and often have to resort to highly error-prone heuristics. Unfortunately, incomplete datasets are a frequent phenomenon, especially when datasets are generated automatically using various information extraction or information integration approaches. Here, the recent trend of crowd-enabled databases promises a powerful solution: during query execution, some database operators can be dynamically outsourced to human workers in exchange for monetary compensation, therefore enabling the elicitation of missing values during runtime. Unfortunately, this powerful feature heavily impacts query response times and (monetary) execution costs. In this paper, we present an innovative hybrid approach combining dynamic crowdsourcing with heuristic techniques in order to overcome current limitations. We will show that by assessing the individual risk a tuple poses with respect to the overall result quality, crowdsourcing efforts for eliciting missing values can be narrowly focused on only those tuples that may degenerate the expected quality most strongly. This leads to an algorithm for computing skyline sets on incomplete data with maximum result quality, while optimizing crowd-sourcing costs.

The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

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